{"id":"f92c2e383823","type":"article","url":"https://hartvaat.nl/2026/09/12/machine-learning-algoritmen-verbeteren-cv-risicoschatting-maar-klinische-meerwaa/","title":"Machine learning-algoritmen verbeteren CV-risicoschatting, maar klinische meerwaarde en overdiagnoserisico blijven onbeantwoord","title_en":"Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease","category":"algemeen","category_label":"Algemeen","professions":["cardioloog","huisarts","internist"],"tags":[],"journal":"Open Heart","doi":"info:doi/10.1136/openhrt-2026-004228","source_url":"https://doi.org/info:doi/10.1136/openhrt-2026-004228","authors":["Phillips","S. P.","Han","H."],"significance":5,"published":"2026-09-21","source_date":"2026-09-12","image":"","kennis":["https://hartvaat.nl/kennis/vasculair/wells-score-dvt-longembolie/","https://hartvaat.nl/kennis/cardiometabool/diabetes-en-cardiovasculair-risico/"],"congress":"","summary_en":"A systematic review of 29 studies evaluated whether machine learning algorithms improve cardiovascular disease risk prediction compared with the Framingham Risk Score (FRS). While 23 studies reported better predictive performance, the authors highlight critical limitations including inconsistent outcome definitions, the addition of costly diagnostics like CT angiography, and a heightened risk of overdiagnosis without clear clinical benefit. For clinical practice, this indicates that AI-driven risk tools are not yet ready for routine implementation and require rigorous validation, cost-effectiveness assessments, and alignment with established screening principles before they can safely augment traditional risk assessment.","created":"2026-09-14T01:17:40Z","updated":"2026-09-14T01:17:40Z","licence":"Citeer vrij, met bronvermelding en een link naar hartvaat.nl (de url van het record). Samenvattingen zijn redactioneel werk van HartVaat; de oorspronkelijke publicaties blijven van hun uitgevers (doi). Geen medisch advies.","body_markdown":"Een systematische review van 29 studies onderzocht of machine learning-algoritmen voor cardiovasculaire risicoschatting beter presteren dan de traditionele Framingham Risk Score (FRS). Hoewel 23 studies een verbeterde voorspellende nauwkeurigheid rapporteerden, wijzen de auteurs op belangrijke hiaten: inconsistente definitie van uitkomsten, het toevoegen van dure tests zoals CT-angiografie, en het risico op overdiagnose door een hogere risicoprognose zonder klinische onderbouwing. Voor de dagelijkse praktijk betekent dit dat AI-tools momenteel nog niet klaar zijn voor routinematige inzet; ze vereisen eerst strikte validatie, kosten-batenanalyses en aansluiting bij bewezen screeningprincipes.","abstract_original":"Objective\n

To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates.

\n\nMethods\n

For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or non-peer-reviewed papers were excluded.

\n

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two reviewers screened titles and abstracts independently, then populated a purpose-built data extraction form. A subsequent qualitative thematic analysis focused on algorithms’ strengths, added value, potential harms, unintended consequences and equity implications.

\n

The main outcome assessed was whether, among healthy adults, the algorithm improved CVD risk prediction relative to the FRS.

\n\nResults\n

Of 707 studies retrieved, 29 met inclusion criteria. 23 reported improved predictive ability relative to the FRS. Most datasets and/or medical records used included sociodemographic predictors of CVD not included among FRS inputs. Some added costly diagnostic tests like CT angiography to FRS screening indicators. When they were defined, inputs and outcomes such as hypertension or myocardial infarction did not always adhere to FRS values. Statistical significance was generally taken as a proxy for clinical significance. Some algorithms overestimated the number at risk compared with the FRS without discussing whether that larger proportion might be at risk of overdiagnosis rather than CVD, while a few decreased the proportion found to be at risk.

\n\nConclusions\n

Use of artificial intelligence to improve accuracy of risk assessment for CVD demonstrates the technological capacity to merge known sociodemographic predictors with biologic variables and examine non-linear interactions among these. Still needed to achieve patient benefit is clinical insight, adherence to screening principles and cost–benefit assessment of inputs selected.

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